I have created an offline airgapped Retrieval-Augmented Generation (RAG) system for secure and private document question-answering, utilizing Llama 3, Mistral, and Gemini, designed for researchers and developers handling sensitive data.
Llama C++, LangChain, Streamlit, Llama 3, Mistral, Gemini
2024-10-11
- website
Highlight 1
The offline capability ensures data privacy, making it an excellent choice for handling sensitive documents.
Highlight 2
The system supports multiple advanced models, providing flexibility and adaptability to various NLP tasks.
Highlight 3
Its user-friendly interface enhances accessibility, allowing users to effectively interact with their documents.
Improvement 1
The addition of more comprehensive documentation could help users better understand setup and usage.
Improvement 2
Implementing onboarding tutorials would assist new users in navigating the system more effectively.
Improvement 3
Expanding the range of supported document formats could enhance functionality for users with diverse needs.
Product Functionality
Consider integrating more document format support to accommodate a wider range of user needs.
UI & UX
Enhancing the onboarding experience with interactive tutorials could help new users familiarize themselves with the system quickly.
SEO or Marketing
Implementing a content marketing strategy that includes case studies and user testimonials can improve visibility.
MultiLanguage Support
Adding multi-language support would greatly increase accessibility for non-English speaking users.
- 1
What is the main purpose of the airgapped RAG system?
The primary purpose of the airgapped RAG system is to provide secure and private question-answering capabilities on documents without relying on cloud services.
- 2
Can I use this system with any type of document?
The system supports various document formats, but expanding compatibility is a planned area for improvement.
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Is this tool suitable for researchers handling sensitive information?
Yes, the tool is specifically designed for researchers and professionals who need to process sensitive data offline to ensure privacy.